Systems and methods for particulate filter regeneration

ABSTRACT

Methods and systems are provided for selecting a first travel route for a vehicle from a database based on particulate filter regeneration requirements and an inferred initial driver state of mind. In one example, the initial driver state of mind may be selected based on a past driver history, and during travel along the first travel route, the driver state of mind may be updated based on the driver interactions with traffic. The route selection may also be updated based on the updated driver state of mind.

FIELD

The present description relates generally to methods and systems forselecting a travel route for a vehicle for particulate filterregeneration, based on driver behavior.

BACKGROUND/SUMMARY

Emission control devices, such as particulate filters (PF), may reducethe amount of soot emissions from an internal combustion engine bytrapping soot particles. Such devices may be passively regeneratedduring operation of the engine to decrease the amount of trappedparticulate matter. However, during vehicle operation, conditions forsustained full regeneration of the PF may not be available. For example,during urban driving conditions which include frequent idle stops andlight load engine operation, frequent premature terminations ofregeneration may occur. Premature terminations of regeneration may occurdue to the driving behavior, such as frequent brake application, of thevehicle driver (herein also referenced to as the operator). Thepremature terminations may result in the need for active regeneration,leading to an increased regeneration fuel penalty.

Various approaches are provided for regenerating a PF during a vehicledrive cycle. In one example, as shown in U.S. Pat. No. 8,424,294,Schumacher et al. disclose a method to control the regeneration cyclesof an exhaust gas after treatment system, such as a particulate filter,based on driver specific information such that an optimal regenerationis achieved. The driver specific information may include driving habits,driving cycles, and driving routes used by the driver. Such driverspecific information may be utilized to predict phases during a drivewhen regeneration of the particulate filter may be possible.

However, the inventors herein have recognized potential disadvantageswith the above approach. As one example, driver specific information maynot remain constant during a drive cycle resulting in a significantdifference between a predicted driver behavior along a travel route anda real-time driver behavior. As a result, a route planned based on thepredicted driver behavior, without accounting for temporal changes indriver behavior, may have a PF regeneration efficiency that is differentfrom the actual PF regeneration efficiency. Also, environmental factorsincluding traffic conditions and weather conditions may significantlyaffect the possibility of completion of regeneration over the drivecycle. Further, selection of a travel route solely based on driverpreferences may result in higher fuel consumption and increased durationof travel.

In one example, the issues described above may be addressed by an enginemethod, comprising: at an onset of a drive cycle, displaying a firstdriving route responsive to each of a particulate filter (PF) loadingand past driving history; and during travel along the first drivingroute, displaying an updated route responsive to each of trafficconditions and a comparison of a driving history along the first routeon the drive cycle relative to the past driving history. In this way, byestimating a driver state of mind in real-time and quantitatively usingthe driver state of mind to recommend routes to a vehicle driver, PFregeneration efficiency may be improved.

As one example, a vehicle controller may develop a route database for avehicle driver as a function of routes that are frequently used, alongwith drive history on each route. Each time a trip is completed, thedatabase may be updated with information regarding drivercharacteristics including driving practices such as pedal input, brakeusage, lane change frequency, vehicle start-stop frequency, etc. At theonset of a drive cycle, an initial state of mind of the driver may bepredicted based on drive history (driver characteristics) as retrievedfrom the database. As such, there may be multiple states of mind of thedriver and there may be a change in the state of mind during the drivecycle based on factors such as traffic and weather conditions. Eachstate of mind may correspond to a distinct PF regeneration factor whichmay directly influence the possibility of attaining a desiredregeneration of PF over a given route. Responsive to an indication of aknown destination (based on driver input) or a predicted destination(based on driving history and route forecasting algorithms) and furtherbased on the current soot level of the PF and the initial driver stateof mind, one or more routes may be selected from the database andhierarchically displayed to the vehicle driver. For a given destination,when the PF load is higher than a threshold and the driver is in a firststate of mind, a first route may provide a higher PF regenerationefficiency while a second route may have a lower PF regenerationefficiency. But for the same destination, and the same higher thanthreshold PF load but a different driver state of mind, that first routemay have a lower PF regeneration efficiency while the second route mayhave a higher PF regeneration efficiency. Navigational instructions maythen be provided based on the driver selection. During the drive cycle,the state of mind may be updated in real-time based on driverinteractions with traffic and environmental conditions such as weather.A non-homogeneous state transformation matrix may be used to determinechanges in state of mind of the driver, during the drive. As the driverstate of mind changes, the regeneration efficiencies of the routes maybe recalibrated and an alternate route that now provides the highest PFregeneration efficiency may be displayed. The ranking of the selectedroutes may be adjusted in real-time based on the current driver state ofmind such that the route at the top of the list may correspond to ahighest degree of attainable PF regeneration.

In this way, by taking into account a current driver state of mind inselecting and ranking routes for regeneration of a particulate filter,the likelihood that a driver will follow the recommended route isincreased. By estimating the driver state of mind in real-time based ondriver interactions with traffic, and environmental conditions andupdating the ranking of the displayed routes, a probability of attainingof a desired level of PF regeneration may be improved. By maintaining adatabase of frequently traveled routes with information including theactual degree of PF regeneration attained on each route and driverhistory on these routes, it may be possible to select one or more routesfrom a database based on PF regeneration requirement during a futuredrive cycle. The technical effect of using a non-homogeneous transitionmatrix to estimate changes in the driver state of mind during the drivecycle is that the constantly evolving traffic scenario may be optimallycaptured while determining the current driver state of mind, and itseffect on the regeneration of the PF. By correlating each distinctdriver state of mind to a regeneration factor, the influence of driverbehavior on the regeneration may be quantified and accounted for duringroute planning and passive PF regeneration. In this way, by estimatingsuitable routes for PF regeneration while taking into consideration theinfluence of driver state of mind, regeneration of the system may beopportunistically carried out, thereby reducing over-loading of soot inthe particulate filter and improving engine performance and particulatefilter health.

It should be understood that the summary above is provided to introducein simplified form a selection of concepts that are further described inthe detailed description. It is not meant to identify key or essentialfeatures of the claimed subject matter, the scope of which is defineduniquely by the claims that follow the detailed description.Furthermore, the claimed subject matter is not limited toimplementations that solve any disadvantages noted above or in any partof this disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows an example embodiment of an engine system including aparticulate filter.

FIG. 2 shows a flow chart illustrating an example method that may beimplemented for selecting a route of travel based on PF regenerationrequirements.

FIG. 3 shows flow chart illustrating an example method that may beimplemented for determining a current state of mind of a driver and theinfluence of the driver state of mind upon route selection.

FIG. 4 shows a flow chart illustrating an example method that may beimplemented for updating a database of frequently travelled routes.

FIG. 5A shows a first example display of suggested routes based on PFregeneration requirements and an initial driver state of mind.

FIG. 5B shows a second example display of suggested routes based on PFregeneration requirements and an updated driver state of mind.

FIG. 6 shows a state machine diagram for transition of the state of mindof the driver.

FIG. 7 shows a transition matrix for changes in the driver state ofmind.

FIG. 8 shows a table of regeneration impact factors corresponding toeach state of mind of the driver.

FIG. 9 shows a table of scaling factors corresponding to eachregeneration impact factor for weightages of cost functions associatedwith a route.

FIG. 10 shows an example of prediction and dynamic selection of aproposed route for PF regeneration.

DETAILED DESCRIPTION

The following description relates to systems and methods for selecting aroute from a database containing details of frequently travelled routesand driver behavior for optimal particulate filter regeneration. Anexample engine system comprising a particulate filter is shown inFIG. 1. An engine controller may be configured to perform controlroutines, such as the example routine of FIG. 2, to select a route oftravel from a database based on PF regeneration requirements. A controlroutine, such as the example of FIG. 3, may be performed to estimate acurrent state of mind of the driver and to further determine theinfluence of the current state of mind on selection of travel routesoptimal for PF regeneration. Following each drive cycle, the controllermay perform a routine, such as the example routine of FIG. 4, to updatethe database with information learnt during the drive cycle. FIGS. 5Aand 5B show example displays of suggested routes based on PFregeneration requirements and a driver state of mind. As shown in thestate machine diagram of FIG. 6, the driver state of mind may changebetween multiple states during a drive cycle and such changes may beestimated based on a transition matrix, as shown in FIG. 7. The distinctdriver states of mind may correspond to distinct regeneration impactfactors as tabulated in FIG. 8 and these regeneration impact factors mayaffect the weightages of cost functions for route determination astabulated in FIG. 9. A prophetic example including prediction andselection of proposed travel routes based on PF regenerationrequirements is shown at FIG. 10.

FIG. 1 schematically shows aspects of a vehicle system 102 with anexample engine system 100 including an engine 10. In one example, theengine system 100 may be a diesel engine system. In one example, theengine system 100 may be a gasoline engine system. In the depictedembodiment, engine 10 is a boosted engine coupled to a turbocharger 113including a compressor 114 driven by a turbine 116. Specifically, freshair is introduced along intake passage 42 into engine 10 via air cleaner112 and flows to compressor 114. The compressor may be any suitableintake-air compressor, such as a motor-driven or driveshaft drivensupercharger compressor. In engine system 10, the compressor is aturbocharger compressor mechanically coupled to turbine 116 via a shaft19, the turbine 116 driven by expanding engine exhaust.

As shown in FIG. 1, compressor 114 is coupled, through charge-air cooler(CAC) 17 to throttle valve 20. Throttle valve 20 is coupled to engineintake manifold 22. From the compressor, the compressed air charge flowsthrough the charge-air cooler 17 and the throttle valve to the intakemanifold. In the embodiment shown in FIG. 1, the pressure of the aircharge within the intake manifold is sensed by manifold air pressure(MAP) sensor 124.

One or more sensors may be coupled to an inlet of compressor 114. Forexample, a temperature sensor 65 may be coupled to the inlet forestimating a compressor inlet temperature, and a pressure sensor 66 maybe coupled to the inlet for estimating a compressor inlet pressure. Asanother example, a humidity sensor 67 may be coupled to the inlet forestimating a humidity of aircharge entering the compressor. Still othersensors may include, for example, air-fuel ratio sensors, etc. In otherexamples, one or more of the compressor inlet conditions (such ashumidity, temperature, pressure, etc.) may be inferred based on engineoperating conditions. In addition, when exhaust gas recirculation (EGR)is enabled, the sensors may estimate a temperature, pressure, humidity,and air-fuel ratio of the aircharge mixture including fresh air,compressed air, and recirculated exhaust residuals received at thecompressor inlet.

A wastegate actuator 92 may be actuated open to dump at least someexhaust pressure from upstream of the turbine to a location downstreamof the turbine via wastegate 90. By reducing exhaust pressure upstreamof the turbine, turbine speed can be reduced, which in turn helps toreduce compressor surge.

Intake manifold 22 is coupled to a series of combustion chambers 30through a series of intake valves (not shown). The combustion chambersare further coupled to exhaust manifold 36 via a series of exhaustvalves (not shown). In the depicted embodiment, a single exhaustmanifold 36 is shown. However, in other embodiments, the exhaustmanifold may include a plurality of exhaust manifold sections.Configurations having a plurality of exhaust manifold sections mayenable effluent from different combustion chambers to be directed todifferent locations in the engine system.

In one embodiment, each of the exhaust and intake valves may beelectronically actuated or controlled. In another embodiment, each ofthe exhaust and intake valves may be cam actuated or controlled. Whetherelectronically actuated or cam actuated, the timing of exhaust andintake valve opening and closure may be adjusted as needed for desiredcombustion and emissions-control performance.

Combustion chambers 30 may be supplied one or more fuels, such asgasoline, alcohol fuel blends, diesel, biodiesel, compressed naturalgas, etc., via injector 69. Fuel may be supplied to the combustionchambers via direct injection, port injection, throttle valve-bodyinjection, or any combination thereof. In the combustion chambers,combustion may be initiated via spark ignition and/or compressionignition.

As shown in FIG. 1, exhaust from the one or more exhaust manifoldsections is directed to turbine 116 to drive the turbine. The combinedflow from the turbine and the wastegate then may flow through exhaustafter-treatment devices 170 and 172. In one example, the first exhaustafter-treatment devices 170 may be a light-off catalyst, and the secondexhaust after-treatment devices 172 may be a particulate filter such asa regeneratable particulate filter (PF). As an example, the PF may be adiesel particulate filter coupled to the exhaust passage 104 of a dieselengine. In another example, the PF may be a gasoline particulate filtercoupled to the exhaust passage 104 of a gasoline engine. The PF may bemanufactured from a variety of materials including cordierite, siliconcarbide, and other high temperature oxide ceramics. As such, the PF hasa finite capacity for holding soot. Therefore, the PF may need to beperiodically regenerated in order to reduce the soot deposits in thefilter so that flow resistance due to soot accumulation does not reduceengine performance. Passive PF regeneration may be favorably carried outduring certain engine operating conditions such as during higher engineload when exhaust gas flowing through the PF is of a defined compositionand is above a threshold temperature, in order to burn or oxidize thetrapped particulate matter. During passive PF regeneration, the soot maybe opportunistically burnt due to the higher exhaust temperature andalso by the presence of a desired amount of oxygen in the exhaust.Filter regeneration may be accomplished by actively heating the filter,by flowing electric current, to a temperature that will burn sootparticles at a faster rate than the deposition of new soot particles,for example, 400-600° C. such as during active PF regeneration. Duringactive regeneration, spark timing may be retarded or fuel enrichment maybe carried out to increase exhaust temperature. Therefore, active PFregeneration may increase fuel consumption and parasitic loss of energy(by supplying electricity to the filter). In comparison, during passiveregeneration, active heating of the PF using electric current, sparkretard, and/or fuel enrichment may not be desired. In one example, thePF can be a catalyzed particulate filter containing a washcoat ofprecious metal, such as platinum, to lower soot combustion temperatureand also to oxidize hydrocarbons and carbon monoxide to carbon dioxideand water.

In one example, the exhaust after-treatment device 170 may be configuredto trap NO_(x) from the exhaust flow when the exhaust flow is lean andto reduce the trapped NO_(x) when the exhaust flow is rich. In otherexample, the exhaust after-treatment device 170 may be configured todisproportionate NO_(x) or to selectively reduce NO_(x) with the aid ofa reducing agent. In yet another example, the exhaust after-treatmentdevice 170 may be configured to oxidize residual hydrocarbons and/orcarbon monoxide in the exhaust flow. Different exhaust after-treatmentcatalysts having any such functionality may be arranged in wash coats orelsewhere in the exhaust after-treatment stages, either separately ortogether. All or part of the treated exhaust from the exhaustafter-treatment devices 170 and 172 may be released into the atmospherevia main exhaust passage 104 after passing through a muffler 174.

Exhaust gas recirculation (EGR) delivery passage 180 may be coupled tothe exhaust passage 104 downstream of turbine 116 to provide lowpressure EGR (LP-EGR) to the engine intake manifold, upstream ofcompressor 114. An EGR valve 62 may be coupled to the EGR passage 180 atthe junction of the EGR passage 180, and the intake passage 42. EGRvalve 62 may be opened to admit a controlled amount of exhaust to thecompressor inlet for desirable combustion and emissions controlperformance. EGR valve 62 may be configured as a continuously variablevalve or as an on/off valve. In further embodiments, the engine systemmay include a high pressure EGR flow path wherein exhaust gas is drawnfrom upstream of turbine 116 and recirculated to the engine intakemanifold, downstream of compressor 114.

One or more sensors may be coupled to EGR passage 180 for providingdetails regarding the composition and condition of the EGR. For example,a temperature sensor may be provided for determining a temperature ofthe EGR, a pressure sensor may be provided for determining a pressure ofthe EGR, a humidity sensor may be provided for determining a humidity orwater content of the EGR, and an air-fuel ratio sensor may be providedfor estimating an air-fuel ratio of the EGR. Alternatively, EGRconditions may be inferred by the one or more temperature, pressure,humidity, and air-fuel ratio sensors 65-67 coupled to the compressorinlet. In one example, air-fuel ratio sensor 57 is an oxygen sensor.

A plurality of sensors, including an exhaust temperature sensor 128 andan exhaust oxygen sensor, and exhaust pressure sensor 129 may be coupledto the main exhaust passage 104. The oxygen sensor may be linear oxygensensors or UEGO (universal or wide-range exhaust gas oxygen), two-stateoxygen sensors or EGO, HEGO (heated EGO), a NOx, HC, or CO sensors.

Engine system 100 may further include control system 14. Control system14 is shown receiving information from a plurality of sensors 16(various examples of which are described herein) and sending controlsignals to a plurality of actuators 18 (various examples of which aredescribed herein). A navigation system 154 such as global positioningsystem (GPS) may be coupled to the control system 14 to determinelocation of the vehicle 102 at key-on and at any other instant of time.The navigation system may be connected to an external server and/ornetwork cloud 160 via wireless communication 150. The navigation system154 may determine the current location of the vehicle 102 and obtainambient condition data (such as temperature, pressure, etc.) and roadinformation (such as road gradient) from a network cloud 160. Thecontroller 12 may be coupled to a wireless communication device 152 fordirect communication of the vehicle 102 with a network cloud 160. Atcompletion of a drive cycle, the database 13 may be updated with routesegment information including driver behavior, driver states of mind, alevel of particulate filter regeneration achieved, engine operatingconditions, date and time information, and traffic information. Further,details of the trip including origin and destination, stops duringtravel and duration of each stop, road gradient (terrain) of the route,fuel consumption, duration of travel, driver behavior, etc. may bestored in a database 13 within the controller 12. Information regardingthe route and traffic may be learned from the navigation system 154 andthe external cloud 160 via wireless communication 150. The differentroutes in the database may be compared and ranked in terms of fuelefficiency, duration of travel, and achievable PF regeneration level.Details regarding a driver driving pattern may be retrieved from thecontroller's memory and used for ranking the routes. Also, the drivingpattern of the vehicle driver may be learned over a number of vehicledrive cycles based on one or more of frequent trip time patterns,habitual probability patterns, route based statistical profile, andenvironmental attribute profiles. Other statistical profiles, differentstates of mind of the driver, and conditions for transitioning from onestate of mind to another may be learned and stored in the database 13.The engine operating parameters may be estimated via inputs from one ormore sensors 16 and the information may be added to the database 13.Details of updating the database 13 are discussed in details withrelation to FIG. 4.

At the onset of a drive cycle (at vehicle key-on), based on PF sootlevel, a requirement for PF regeneration during the upcoming drive cyclemay be assessed and in response to the driver providing a destination(such as via an input to an on-board navigation system), one or moreroutes may be selected from the database 13 based on the driver state ofmind, to facilitate a higher degree of PF regeneration while optimizingfuel efficiency and time of travel. The route selection may be furtherbased on a driver selected cost function including highest fuelefficiency and lowest travel time for the drive cycle. The one or moreroutes selected may then be ranked as a weighted function of each of aparticulate filter regeneration efficiency, a probability of completionof a PF regeneration event, a fuel efficiency, and a travel time of eachof the one or more routes. As an example, if based on the current PFsoot level, it is inferred that a PF regeneration is desired during theupcoming drive cycle, the highest ranked route displayed to the operatormay be a route that enables the destination to be reached whileproviding the highest degree of regeneration and while providing somedegree of fuel economy. During situations when the driver does notselect a route from the one or more routes recommended (displayed) tothe driver, an upcoming route segment may be dynamically predicted basedon a driving history (of driver) retrieved from the database. Also, thedriver may select a route from the one or more recommended routes andinitiate travel along the route, and then deviate from the selectedroute. During such deviations, the controller may dynamically predict anupcoming route segment based on a driver driving history (such aspreferred routes of travel during a specific time of day or day of week)as retrieved from the database. One or more routes or route segments maybe selected from the database based on the predicted destination, rankedin terms of their particulate filter regeneration efficiency,probability of completion of a PF regeneration event, fuel efficiency,and travel time of each of the one or more routes and displayed to thedriver. Details of route selection based on information stored in thedatabase 13 are discussed with relation to FIG. 2.

In this way, in response to a driver destination selection indicated viaa display of a vehicle, a particulate filter soot load may be estimated,a current location of the vehicle may be determined, one or more routesmay be retrieved from the current location to the destination from adatabase, the one or more routes may be ranked based on each of aparticulate filter regeneration efficiency, fuel efficiency, and time oftravel of each route; and the one or more routes to the selecteddestination may be displayed to the driver in order of their rank.

The selection and ranking of the one or more routes may be further basedon a driver state of mind. A driver state of mind may represent areal-time driving behavior of the driver, such as in one state of mindthe driver may drive more aggressively while in a different state ofmind, the driver may be more relaxed during the drive cycle. At an onsetof a drive cycle, a first driver state of mind may be selected from aplurality of driver states of mind stored in the database 13 based on apast driving history of the driver, traffic conditions at the drivecycle origin, and environmental conditions at the drive cycle originincluding temperature, humidity, precipitation, etc. The past drivinghistory of the driver includes routes traveled by the operator as afunction of one or more of a time of a day, the day of a week, the drivecycle origin and destination, and drive characteristics includingfrequency of brake usage, average acceleration force used, and averagelane change frequency. The first driver state of mind may correspond toa first particulate filter regeneration factor. Based on the firstparticulate filter regeneration factor, the selection, and ranking ofthe one or more routes may be updated. Updating the ranking of the oneor more routes includes, ranking each of the one or more routes based ona weighted function of each of the respective regeneration completionefficiencies, a probability of completion of a particulate filterregeneration event, fuel efficiency, and a time to destination of eachof the one or more routes, the weighted function scaled based on thefirst regeneration factor. Once a driver selects a route from the rankedone or more routes, navigational instructions for the operator selectedroute may be displayed to the driver.

As the driver travels along the driver selected route, real-time driverinteractions with traffic including one or more of frequency of stops,frequency of lane changes, accelerator pedal input, and brake inputduring the drive cycle may be learned. Based on the learned real-timedriver interactions with traffic and a comparison of the real-timedriver interactions with traffic during travel along the driver selectedroute relative to the past driving history, the driver state of mind maybe updated from the first state to a second state of mind, the secondstate of mind also selected from the database 13. The updated, seconddriver state of mind may correspond to a second particulate filterregeneration factor. In response to the change in the driver state ofmind, the ranking of the one or more routes may be updated as theweighted function of each of the respective regeneration completionefficiencies, the probability of completion of the particulate filterregeneration event, fuel efficiency, and the time to destination of eachof the one or more routes may be scaled based on the second regenerationfactor. For example, in the first ranking, the highest ranked routedisplayed to the operator may be a route that provides the highestdegree of PF regeneration, however, once the ranking is updated inresponse to the change in the driver state of mind, the previouslyhighest ranked route may no longer provide the highest degree of PFregeneration and a different route enabling the highest degree of PFregeneration may now be ranked first. The updated ranking of the one ormore routes may then be displayed to the driver for further selection.At completion of the drive cycle, a degree of PF regeneration attainedduring the drive cycle may be learned and the database 13 may be updatedwith the learned degree of PF regeneration attained for the drive cycle,the first driver state of mind, and the updated driver state of mind.

The control system 14 may include a controller 12. The controller 12 mayreceive input data from various sensors 18, process the input data, andtrigger various actuators 81 in response to the processed input databased on instruction or code programmed therein corresponding to one ormore routines. As one example, sensors 16 may include exhaust gas oxygensensor located upstream of the turbine 116, pedal position sensor, MAPsensor 124, exhaust temperature sensor 128, exhaust pressure sensor 129,oxygen sensor, compressor inlet temperature sensor 65, compressor inletpressure sensor 66, and compressor inlet humidity sensor 67. Othersensors such as additional pressure, temperature, air-fuel-ratio, andcomposition sensors may be coupled to various locations in engine system100. The actuators 81 may include, for example, throttle 20, EGR valve62, wastegate 92, and fuel injector 69. As an example, during key-on,based on a soot level on the PF as estimated via the exhaust pressuresensor 129, the controller may select an optimal route for the drivecycle based on information stored in the database and input from thenavigation system 154 and the network cloud 160. The controller may thendisplay the optimal route to the driver and if the route is selected, alevel of PF regeneration achieved during the trip may be monitored andlearned.

In some examples, vehicle 102 may be a hybrid vehicle with multiplesources of torque available to one or more vehicle wheels 55. In otherexamples, vehicle 102 is a conventional vehicle with only an engine, oran electric vehicle with only electric machine(s). In the example shown,vehicle 102 includes engine 10 and an electric machine 52. Electricmachine 52 may be a motor or a motor/generator. Crankshaft of engine 10and electric machine 52 are connected via a transmission 54 to vehiclewheels 55 when one or more clutches 56 are engaged. In the depictedexample, a first clutch 56 is provided between crankshaft 140 andelectric machine 52, and a second clutch 56 is provided between electricmachine 52 and transmission 54. Controller 12 may send a signal to anactuator of each clutch 56 to engage or disengage the clutch, so as toconnect or disconnect crankshaft from electric machine 52 and thecomponents connected thereto, and/or connect or disconnect electricmachine 52 from transmission 54 and the components connected thereto.Transmission 54 may be a gearbox, a planetary gear system, or anothertype of transmission. The powertrain may be configured in variousmanners including as a parallel, a series, or a series-parallel hybridvehicle.

Electric machine 52 receives electrical power from a traction battery 58to provide torque to vehicle wheels 55. Electric machine 52 may also beoperated as a generator to provide electrical power to charge battery58, for example during a braking operation.

In this way, the components of FIG. 1 enables a vehicle systemcomprising: a vehicle, a navigation system wirelessly connected to anexternal network, a display, an engine including an intake system and anexhaust system, the exhaust system including a particulate filter (PF)coupled to an exhaust passage and a pressure sensor coupled to theexhaust passage upstream of the particulate filter, and a controllerwith computer readable instructions stored on non-transitory memory for:at an onset of a drive cycle, displaying a first route based on PF loadand a first driver state of mind, and responsive to driver interactionswith traffic while travelling on the first route, displaying a pluralityof updated routes based on a second driver state of mind, wherein thefirst driver state of mind is selected from a database based on each ofthe PF load and a driver history and a change from the first driverstate of mind to the second driver state of mind is based on the driverinteractions with traffic while traveling on the first route.

FIG. 2 shows an example method 200 for selecting a route of travel basedon particulate filter (PF) regeneration requirements. Instructions forcarrying out method 200 and the rest of the methods included herein maybe executed by a controller based on instructions stored on a memory ofthe controller and in conjunction with signals received from sensors ofthe engine system, such as the sensors described above with reference toFIG. 1. The controller may employ engine actuators of the engine systemto adjust engine operation, according to the methods described below.

At 202, the routine may include determining if a vehicle key-on event isdetected. For example, it may be determined that the vehicle driver hasexpressed intent to start vehicle operation. As such, by confirming avehicle key-on event, an upcoming vehicle drive cycle is indicated.While referred to herein as a vehicle “key-on” event, it will beappreciated that the driver may indicate intent to operate the vehiclewith or without the use of a key. For example, vehicle operation may beinitiated by inserting a key (active key) into an ignition slot andmoving the slot to an “ON” position. Alternatively, vehicle operationmay be initiated when a key (passive key) is within a threshold distanceof the vehicle (e.g., in the vehicle). As another example, vehicleoperation may be initiated when the driver presses an ignition button toan “ON” position. Still other approaches may be used by a driver toindicate intent to operate the vehicle. As such, vehicle driver drivingpatterns may only be learned when the vehicle is operating. Thus, if avehicle key-on event is not confirmed, and therefore an upcoming vehicledrive cycle, is not confirmed, the method may end and PF regenerationmay not be carried out.

If a key-on event is confirmed, at 204, current vehicle and engineoperating conditions may be estimated and/or measured. These mayinclude, for example, engine speed, vehicle speed, engine temperature,engine load, ambient conditions (ambient humidity, temperature, andbarometric pressure), boost level, exhaust temperature, manifoldpressure, manifold air flow, battery state of charge, etc.

At 206, the level of soot accumulated in an exhaust PF may be estimatedbased on input from an exhaust pressure sensor (such as pressure sensor129 in FIG. 1) positioned upstream of the PF. As the soot level in thePF increases, the exhaust back pressure may increase pumping lossesthereby affecting engine performance and increasing fuel consumption.Therefore, if the soot level increases to above a threshold, the PF maybe regenerated by burning at least a portion of the soot deposited onit. However, passive regeneration of PF may be adversely affected duringengine operating conditions such as idling, lower engine load, and lowerengine temperature. Incomplete or aborted PF regenerations may adverselyaffect engine efficiency. Therefore, when a new trip is initiated, aroute of travel may be selected taking into consideration the soot levelon the PF such that PF regeneration may be opportunistically carried outduring the trip.

At 208, the routine includes determining if a destination has beenspecified by the driver. The driver may specify a destination via aninput to the on-board navigation system. If it is determined that thedestination is known, the routine proceeds to step 210 to retrieve oneor more routes between the origin and the destination from a database(such as database 13 in FIG. 1). The origin (such as coordinates,geographical location) may be determined from the on-board navigationsystem or from a network cloud via a wireless connection. The databaseis maintained updated with information of frequently travelled vehicleroutes. Information including origin and destination, routes taken,stops during trip and duration of each stop, traffic information foreach route, day and time of travel, engine operating conditions, fuelconsumption, duration of travel, possible degree of PF regeneration,driver driving characteristics, etc. may be available in the database.An example method of updating the database during each trip iselaborated with reference to FIG. 4. Given the current vehicle positionand the destination (as indicated by the driver) there may be aplurality of possible routes available in the database.

At 212, one or more routes may be selected from the database for travelbetween the current vehicle position and the destination. A dynamicprogramming may be carried out to estimate the cost associated with eachroute. As an example, the cost function associated with a route may beestimated by Equation 1.J _(A−B)=Σ_(A) ^(B) w ₁(1−E(θ))+w ₂ m _(fuel) ^(regen) +w ₃ m _(fuel)^(A−B) +w ₄ t _(A−B) +w ₅ P(A _(bort)|θ<θ*)  (1)where J_(A−B) is the total cost function (sum of individual costfunctions) associated with a particular route from origin A anddestination B, E(θ) is the expected PF regeneration level of this routefrom point A to B, w₁ is the weightage associated with the expected PFregeneration level of this route, w₂m_(fuel) ^(regen) is the cost toheat the PF to a temperature at which regeneration may commence,w₃m_(fuel) ^(A−B) is the fuel consumption for travel associated with theroute from A to B including fuel usage due to back pressure relatedpumping losses with increasing soot load on the PF, w₄t_(A−B) is thetime duration of the trip from A to B following the particular route,P(A_(bort)|θ<θ*) is the probability of a PF regeneration process to beterminated during travel from point A to B via this particular route,and w₅ is the weightage associated with the probability of PFregeneration termination.

The weightages w₁ and w₅ may be adjusted based on the PF soot level. Inone example, when the current PF soot level is above a threshold and PFregeneration is desired during the upcoming drive cycle, each of theweightages w₁ and w₅ may be increased and the weightages w₃ and w₄ maybe decreased in order to increase the cost functions of routes withlower expected PF regeneration level or with a higher probability oftermination of the PF regeneration process. However, when PFregeneration is not desired during the upcoming drive cycle, theweightages w₁ and w₅ may be decreased while the weightages w₃ and w₄ maybe increased in order to give preference to routes with lower time oftravel and higher fuel efficiency.

The weightages w₁ and w₅ may be further adjusted based on a currentstate of mind of the driver. A state of mind of a driver may influencethe driving characteristics such as accelerator pedal application andrelease frequency, gear change frequency, brake application frequencywhich may further affect PF regeneration. A state of mind of a drivermay correspond to a regeneration impact factor (RIF) and each RIF may inturn correspond to a scaling factor for each of the weightages w₁ andw₅. In one example, if the driver is in a first state of mind duringwhich his driving characteristics may be optimal for PF regeneration(such as lower pedal application and release frequency, lower gearchange frequency, and lower brake application frequency), thecorresponding RIF may result in an equal scaling factor for each of theweightages w₁ to w₅ such that the effect of the driver state of mind maynot change the total cost function associated with the route. As anexample, equal scaling factor of 0.2 may be assigned to each of theweightages w₁ to w₅. Since the scaling factors are equal for all theweightages (the sum of the scaling factors are always equal to one), itmay be inferred that the state of mind of the driver may not have anydetrimental effect on the total cost function of any route. In anotherexample, if the driver is in a second state of mind during which hisdriving characteristics may adversely PF regeneration, the RIFcorresponding to the second state of mind may result in unequal scalingfactors for each of the weightages w₁ and w₅. In order to incorporatethe adverse effect of the driver state of mind in the cost functionestimation for the route, the corresponding RIF may result in a higherscaling factor for w₁ (the weightage associated with the expected PFregeneration level of this route) and w₅ (the weightage associated withthe probability of PF regeneration termination) relative to scalingfactors assigned to other weightages (w₂, w₃, and w₄). By assigning ahigher scaling factor for w₁ and w₅, the individual cost functionsrelating to each of the expected PF regeneration level of this route andthe probability of PF regeneration termination may be increased relativeto the individual cost functions relating to other factors such as timeof travel and fuel usage. In this way, the current state of mind of thedriver may be quantitatively factored into estimation of the total costfunction associated with each route between the origin and adestination. Details regarding a real-time determination of a driverstate of mind and the influence of the driver state of mind on routeselection is described in detail in FIG. 3.

As such, traffic information such as signal phase and timing information(SPaT), as available from the external server or the navigation systemmay be taken into account while estimating the probability of a PFregeneration process being terminated over a given drive cycle and topredict the duration of travel. For example, higher number of trafficstops and traffic congestion in general may increase both theprobability of termination of a passive PF regeneration event and theduration of travel. The controller may determine each of the expected PFregeneration level and the probability of termination of the PFregeneration through a determination that directly takes into accounttraffic situation, such as increasing expected PF regeneration level anddecreasing the probability of termination of the PF regeneration with adecrease in the number of traffic stops. The controller mayalternatively determine each of the expected PF regeneration level andthe probability of termination of the PF regeneration based on acalculation using a look-up table with the input being current trafficsituation and the output being the expected PF regeneration level andthe probability of termination of the PF regeneration.

Once the cost functions are estimated for the plurality of availableroutes between the origin and the destination, the routes may be rankedbased on the cost function, the highest ranked route corresponding tothe lowest cost function. In one example, when PF regeneration isdesired during the upcoming drive cycle, such as when the PF soot levelis higher than a threshold, a route with the highest expected PFregeneration level and lowest probability of termination of theregeneration event may be ranked highest. The highest ranked(recommended) route may be a route that enables the destination to bereached without any significant delay while providing the highest degreeof regeneration and some degree of fuel economy. The subsequent routemay provide a relatively lower degree of regeneration while stillproviding some degree of fuel economy, and so on. In another example,when PF regeneration is not desired during the upcoming drive cycle suchas when the PF soot level is lower than the threshold, the selectedroutes that are displayed may be ranked based on the time taken to reachthe destination and/or fuel cost, and the recommended route may beselected independent of its ability to complete PF regeneration. Thus,the highest ranked (recommended) route may be a route that enables thedestination to be reached in the shortest amount of time or using theleast amount of fuel.

Once the one or more routes have been selected from the database andranked, at 214, the selected routes may be displayed in the order oftheir ranks to the driver. The screen and user interface of the on-boardnavigation system may be used to display the selected routes to theoperator.

At 216, the routine includes determining if the driver has selected aroute from the list of recommended (displayed) routes. If it isdetermined that the driver has selected one of the recommended routes,the routine proceeds to 218 wherein navigational instructions for theselected route is provided to the driver.

At 220, as the driver follows a selected route, in addition tooccurrence of passive regeneration, the controller may also schedule anactive regeneration of the PF during the travel from the origin to thedestination. The scheduling may be based on the soot level and theupcoming road conditions and corresponding engine operating conditions.As an example, PF regeneration may be scheduled once the soot levelincreases to above a threshold level and the driving conditions arefavorable for PF regeneration such as when the engine load is higherthan a threshold load and the engine temperature is higher than athreshold temperature. During the scheduled PF regeneration, thetemperature of the exhaust may be increased by routing electricitythrough the PF to burn the soot accumulated on the PF thereby reducingthe soot load on the PF. The PF may be passively regenerated during thetrip when the exhaust temperature is higher than a threshold and is of adesired chemical composition facilitating oxidation of the sootdeposited on the PF.

At 222, the routine includes determining if the driver has taken adetour (deviation) from the expected route. Each of passive and activeregeneration of the PF regeneration may be affected by unexpectedchanges in driving and traffic conditions. In one example, the detourmay have a higher number of traffic signals compared to the expectedroute, and the frequent traffic stops may adversely affect PFregeneration. If it is determined that a detour is not taken, thescheduled PF regeneration may be continued and at 226, and the routineincludes determining if the expected destination is reached. If it isdetermined that the expected destination has not been reached, at 224,the scheduled PF regeneration may be continued.

Once it is confirmed that the destination is reached, at 228, the sootloading on the PF may be estimated via the exhaust pressure sensor andthe data may be updated. By estimating the remaining soot level in thePF, it is possible to estimate how much soot has been burnt during theregeneration process. Based on the level of soot removal during thedrive cycle, it is possible to estimate the PF regeneration levelachieved during travel along this route, as well as the probability of aPF regeneration process being terminated during travel along this route.

At 230, the route database may be updated with information includingfuel consumption during travel along this route, time of travel(duration), traffic information, PF regeneration schedule, level of PFregeneration achieved, and the states of mind of the driver includingconditions triggering a change in state of mind. An example method ofupdating the database after each trip is elaborated with reference toFIG. 4.

Returning to 208, if it is determined that a destination is not providedby the driver, the routine proceeds to 232 to predict a possibledestination based on driver history as stored in the database. As anexample, the prediction may be carried out while taking into account thecurrent vehicle location, frequently travelled routes during theparticular time of the day and the day of the week, and a driver stateof mind. Traffic conditions (such as traffic congestion) and weatherconditions (such as rain or snow forecast) in the neighborhood of thecurrent vehicle location may also be taken into account while predictingthe destination. In one example, the prediction may be carried out inincrements during the trip. The vehicle controller may divide the routeinto route segments and predict an expected destination for an initialroute segment. Greedy algorithm may be used by taking optimal pathsegments to predict intermediate points on the way to a destination.

If at 222 it is determined that a detour has been taken and the vehicleis no longer travelling to the expected destination via the expectedroute (as selected in step 216), the routine may also proceed to step232 where a final destination or intermediate points may be predictedbased on information available in the database. At 234, based on thepredicted destination (or the upcoming intermediate point), thecontroller may use stochastic dynamic programming to update the one ormore routes selected for reaching the predicted destination. Theselection process may follow the algorithm as shown in step 212 usingequation 1. Once the total cost functions are estimated for theplurality of routes between the current location of the vehicle and thepredicted destination, the routes may be ranked based on the costfunction, the highest ranked route corresponding to the lowest totalcost function. When a PF regeneration is desired during the upcomingsegment of the trip, a route with the highest expected PF regenerationlevel and lowest probability of termination of the regeneration eventmay be the highest recommended route. Once the one or more routes havebeen updated and ranked, the routine proceed to step 214 wherein theselected routes may be displayed to the driver in order of their ranks.At 216, if any of the recommended routes are not accepted by the driver,at 236, the controller may predict the route based on driver preference(corresponding to a driver state of mind), current traffic, and weatherconditions at the current location and at the destination. As anexample, the driver may prefer to take a certain route during a sunnyday during weekday mornings. In another example, intermediate routesegments may be predicted based on driver history as retrieved from thedatabase. The controller may then schedule a PF regeneration event basedon the predicted route segment.

In this way, in response to a destination for a drive cycle not beingindicated by a driver, a current location of the vehicle may bedetermined, a driving history of the driver may be retrieved, adestination may be predicted based on the driving history, selection ofone or more upcoming route segments may be dynamically updated based onthe current location of the vehicle relative to the predicteddestination, the route segments may be ranked based on each of aparticulate filter regeneration efficiency, fuel efficiency, and time oftravel, and displayed to the driver, the one or more route segments tothe predicted destination displayed in order of their rank.

FIG. 3 shows an example method 300 for a real-time estimation of a stateof mind of a driver and the influence of the driver state of mind uponroute selection for a trip. The method 300 may be a part of the method200 and may be carried out at step 212 of method 200. The routeselection may be further based on the soot load accumulated on theparticulate filter (PF).

At 302, the controller may learn a date of travel, the time of travelincluding a time of day when the vehicle is travelling, which day of theweek the vehicle is travelling, etc. The controller may learn thisinformation from an on-board navigation system (e.g., GPS device) orfrom a network cloud via a wireless connection. At 308, the controllermay learn origin characteristics including the geographic location,current weather conditions, and traffic conditions. For example, basedon information from the vehicle navigation system or the network cloud,the controller may determine the origin characteristics. In one example,the geographical location of the origin may include the GPS co-ordinatesof the origin. The weather conditions may include temperature, humidity,wind speed, and precipitation (such as rain, snow, etc.). The trafficconditions may include speed limit of the road the vehicle is beingoperated, the velocity of overall traffic movement, average distancebetween vehicles, traffic congestions, etc. Also, the controller maylearn the geographic location of the destination based on the driver'sinput to a navigation system.

At 310, driver history including driving characteristics of the drivermay be retrieved from the database (such as database 13 in FIG. 1). Inone example, a driver may be identified by the specific key used by thedriver for operating the vehicle. In another example, a driver may beidentified based on the time, day, date of travel and the geographicallocation of origin. As such, a particular driver (such as driver 1) mayoperate the vehicle during weekdays at a specific time (or time window)of the day while a different driver (such as driver 2) may operate thevehicle during weekends and during a certain time window. Thecharacteristics of the driver may include frequency of brake usage,average acceleration force used, average lane change frequency, etc. Thecharacteristics of the driver may vary based on the time, day, date oftravel, weather, and the geographical location of origin. In oneexample, during a sunny day, the driver may drive more aggressively(such as frequent acceleration, increased lane changes, higher speed)compared to his driving style during rain. Further preferences of thedriver, including frequently travelled routes, stops made during thetravel, etc., may also be retrieved from the database. In one example, adriver may travel to a specific destination every weekday morning. Ifthe driver leaves the origin at a certain time, the driver may typicallymake a stop on the way to the destination. However, if the driver leavesthe origin at a later time, the driver may drive more aggressively tothe same destination without making any stops. As such, the driver'stime constraints may be different for different days of the week, suchas, the driver may have a higher time constraint during weekdayscompared to weekends. In another example, the driver's preferences maydepend on weather conditions, such as the driver may take a differentroute than the frequently travelled route (while traveling from theorigin to a frequently travelled destination) if it is snowing or ifthere is forecast for snow for example to avoid road segments that havea grade. In yet another example, the driver's preferences may depend ontraffic conditions, the driver may take a different route than thefrequently travelled route if there is traffic congestion at the origin.In a further example, the driver may select a route based on the fuellevel, such as the driver may select a shorter route if the fuel levelin the tank is lower than a threshold level. In a yet further example,the route selection may be based on the payload of the vehicle such asbased on the number of passengers in the vehicle or if a trailer isbeing towed. The database is updated with information regarding thecharacteristics and preferences of the driver.

At 312, based on the retrieved information including driver history(characteristics of the driver and preferences of the driver), the dayand time of travel, origin characteristics (weather conditions andtraffic conditions), the controller may assign an initial state of mindto the driver. The state of mind of the driver may directly influencethe driver's behavior (driving style) during the trip which may affectPF regeneration and the probability of termination of the regeneration.The states of mind are contextual and probabilistic and each driver mayhave a plurality of states of mind (S_(DK), K=1,2,3, . . . , n) and eachstate may have a different effect on PF regeneration. In one example, adriver may have three distinct states of mind, a first state S_(D0), asecond state S_(D1), and a third state S_(D2). The first state of mind(S_(D0)) may correspond to an optimal state for PF regeneration. Whenthe driver is operating in this optimal state of mind, the drivingcharacteristics (such as frequency of brake usage, average accelerationforce used, average lane change frequency, etc.) may facilitate PFregeneration and may not increase the probability of termination of theregeneration. For example, in the optimal state, the driver may operatethe vehicle at a steady speed for longer durations while applying thebrakes less frequently, the driver may not accelerate or deceleratewithin a short time, and may not change lanes frequently. In thisoptimal state of mind, passive regeneration of the PF may be carried outto a higher level enabling a more complete cleaning of the PF. Thesecond driver state of mind (S_(D1)) may correspond to a sub-optimalstate for PF regeneration where the regeneration level achieved is lowerthan the regeneration level achieved in the first state of mind. Whenthe driver is operating in this sub-optimal state of mind, the drivingcharacteristics (such as frequent accelerating and decelerating,changing lanes, making stops) may reduce the PF regeneration leveland/or may increase the probability of early termination of theregeneration. The third driver state of mind (S_(D2)) may correspond toa least-optimal state for PF regeneration where the regeneration levelachieved is lower than the regeneration levels achieved in each of thefirst state of mind and the second state of mind. When the driver isoperating in this least-optimal state of mind, the drivingcharacteristics (such as frequent braking, travelling at a lower speed)may further lower the PF regeneration level and/or may further increasethe probability of early termination of the regeneration. In thisleast-optimal state, passive regeneration of the PF may be frequentlyinterrupted and regeneration may not be carried out to a desired level.

Since the driver state of mind influences the PF regeneration, aregeneration impact factor may be correlated with each driver state ofmind. At 314, an initial regeneration impact factor corresponding to theinitial state of mind may be determined. In one example, the controllermay use a look-up table to determine the initial regeneration impactfactor corresponding to the initial driver state, with input being theinitial driver state of mind and the output being the regenerationimpact factor. FIG. 8 shows an example table 800 of regeneration inputfactors corresponding to each state of mind. The first row 802 shows afirst regeneration impact factor f₀ corresponding the optimal state ofmind (S_(D0)). The second row 804 shows a second regeneration impactfactor f₁ corresponding the sub-optimal state of mind (S_(D1)). Thefirst row 806 shows a third regeneration impact factor f₂ correspondingthe least-optimal state of mind (S_(D2)).

At 316, a first set of scaling factors corresponding to the initialregeneration impact factor may be determined. Each regeneration impactfactor may have a corresponding set of scaling factors that may beapplied to weightages used for cost function calculation for each routebetween an origin and a destination. As shown in step 212 of FIG. 2,based on a PF regeneration requirement, one or more routes may beselected from the database for travel between the current vehicleposition and the destination. A dynamic programming may be carried outto estimate the total cost associated with each route of the one or moreselected routes. As an example, the total cost function associated witha route may be estimated by Equation 1. The total cost functionassociated with a particular route between an origin and a destinationmay be a summation of individual cost functions corresponding to each ofan expected PF regeneration level of this route from origin todestination, probability of a PF regeneration process to be terminatedduring the drive cycle, cost to heat the PF to a temperature at whichregeneration may commence, fuel consumption, and duration of the tripbetween the origin and destination. Weightages corresponding toindividual cost functions may be adjusted based on the scaling factorscorresponding to a regeneration impact factor. In one example, a scalingfactor may be multiplied to the corresponding weightage of a costfunction to determine a scaled weightage.

FIG. 9 shows a table 900 of scaling factors corresponding toregeneration impact factors and cost function weightages. The firstcolumn 902 of table 900 lists weightages associated with an individualcost function for a route between an origin and a destination. Thesecond column 904 lists the individual cost components of the total costfunction. The first weightage w₁ may be associated with the expected PFregeneration level of a route, the second weightage w₂ may be associatedwith cost to heat the PF to a temperature at which regeneration maycommence, the third weightage w₃ may be associated with fuelconsumption, the fourth weightage w₄ may be associated with timeduration, and the fifth weightage w₅ may be associated with probabilityof a PF regeneration process to be terminated during travel in theroute.

The third column 906 of the table shows the first set of scaling factorscorresponding to the first regeneration impact factor (f₀) for eachweightage. For the first regeneration impact factor (f₀), equal scalingfactors of 0.2 may be assigned to each of the weightages w₁ to w₅. Sincethe scaling factors are equal for all the weightages and the sum of thescaling factors is maintained at 1, it may be inferred that the firstregeneration impact factor (f₀) may not have any significant effect onthe individual cost functions and the total cost function of any route.In other words, for the first regeneration impact factor (f₀), theoperator may not enforce any specific trip criteria as critical andtherefore costs associated with all the trip parameters may be weightedequally.

The fourth column 908 of the table shows the second set of scalingfactors corresponding to the second regeneration impact factor (f₁) foreach weightage. For the second regeneration impact factor (f₁), thescaling factors may be unequally distributed while maintaining the sumof the scaling factors at 1. The scaling factors assigned to each of w₁(PF regeneration level), w₂ (cost of regeneration), and w₅ (probabilityof regeneration termination) may be 0.25 while the scaling factorsassigned to each of w₃ (fuel consumption) and w₄ (trip duration) may be0.125. By assigning a higher scaling factor to each of w₁, w₂, and w₅,the individual costs associated with PF regeneration efficiency of theroute may be increased relative to the individual cost functionsrelating to other factors such as time of travel and fuel usage. Basedon the increase in the cost functions associated with PF regenerationefficiency of the route, it may be inferred that the second regenerationimpact factor (f_(a)) may adversely affect the PF regenerationefficiency for the route if a regeneration is attempted.

The fifth column 910 of the table shows the third set of scaling factorscorresponding to the third regeneration impact factor (f₂) for eachweightage. For the third regeneration impact factor (f₂), the scalingfactors may be unequally distributed while maintaining the sum of thescaling factors to be 1. The scaling factor assigned to w₁ (PFregeneration level) may be 0.35, the scaling factor associated with w₂(cost of regeneration) may be 0.15, and the scaling factor associatedwith w₅ (probability of regeneration termination) may be 0.5 while thescaling factors assigned to each of w₃ (fuel consumption) and w₄ (tripduration) may be 0. By assigning a zero scaling factor to trip durationand fuel consumption and increasing the scaling factors for w₁, w₂, andw₅, the individual cost functions associated with PF regenerationefficiency of the route may be further increased while the individualcost functions relating to time of travel and fuel usage may not beaccounted for in the total cost function estimation. The thirdregeneration impact factor (f₂) may significantly affect the PFregeneration efficiency for the route as probability of regenerationtermination is higher and the total cost function for the route iscalculated completely based on the PF regeneration efficiency therebyincreasing the trip cost of a regeneration is attempted under theseconditions.

At 318, the weightages scaled by the first set of scaling factorscorresponding to the initial regeneration impact factor may be used toestimate a total cost function associated with each route between theorigin and a destination, as selected from the database. Estimation of atotal cost function based on individual cost functions associated withexpected PF regeneration level of a route, probability of a PFregeneration process to be terminated during the drive cycle, cost toheat the PF to the regenerating temperature, fuel consumption, andduration of the trip may be carried out using Equation 1. A plurality ofroutes between the origin and the destination may be selected from thedatabase and the total cost function may be estimated for each of theplurality of routes. The details of total cost function estimation isdescribed in step 212 of FIG. 2.

Once the total cost functions are estimated for the plurality ofavailable routes between the origin and the destination, the routes maybe ranked based on the total cost function, the highest ranked routecorresponding to the lowest total cost function. In one example, when aPF regeneration is desired during the upcoming drive cycle, such as whenthe PF soot level is higher than a threshold, a route with the highestexpected PF regeneration level and lowest probability of termination ofthe regeneration event may be ranked highest. The highest ranked(recommended) route may be a route that enables the destination to bereached without any significant delay while providing the highest degreeof regeneration and some degree of fuel economy. The subsequent routemay provide a relatively lower degree of regeneration while stillproviding some degree of fuel economy, and so on. Once the one or moreroutes have been selected from the database and ranked, the selectedroutes may be displayed in the order of their ranks to the driver. Thescreen and user interface of the on-board navigation system may be usedto display the selected routes to the operator.

Turning briefly to FIG. 5A, it shows a screenshot 500 of an exampleon-board navigation system displaying the ranked route options. In thisexample, PF regeneration is desired during the current drive cycle andtherefore a route with the highest expected PF regeneration level andlowest probability of termination of the regeneration event may beranked first. Further, in this example, the driver state of mind may bethe optimal, first state (S_(D0)) and therefore there may not be asignificant (detrimental) influence of the driver state of mind in theroute rankings. Four routes have been selected from the database andranked in order of their expected PF regeneration level in rows 502,504, 506, and 508, respectively. In each column, a first box 501indicates the suggested route between the origin and the destination, asecond box 503 shows a PF regeneration percentage achievable during thedrive, and a third box 505 shows the time required to reach thedestination from the origin. The driver may select one of the fourroutes via a fourth box included in each row. The driver may select thefourth box of any one of the four routes via a user interface such as atouch function on the screen.

The first route, as shown in row 502, corresponds to the highestexpected PF regeneration level but the trip duration is the longest. Thefourth route, as shown in row 508, corresponds to the lowest expected PFregeneration level but the trip duration is the shortest. Each of thesecond route, as shown in row 504, and the third route, as shown in row506, correspond to intermediate levels of expected PF regeneration leveland trip duration. In this example, the driver selects the first route(row 502) corresponding to the highest expected PF regeneration. Basedon the selection made by the driver, it may be inferred that the driveris not under a time constraint during this drive cycle and it ispossible to schedule PF regeneration during the drive cycle. If a detouris taken where the driver starts on the selected route but does notfollow the selected route, as discussed below, the controller mayconsider the current driver preference of selecting a route that enablesa higher degree of PF regeneration while predicting destination (basedon driver history) and ranking routes.

Returning to FIG. 3, during the drive cycle, in order to determine acurrent state of mind of the driver, at 320, the driver's real-timeinteractions with traffic may be determined. The driver state of mindmay vary from one state of mind to another based on the driver'sinteraction with traffic, and further based on environmental factors(such as weather), and behavioral factors (such as reaction to a certainsituation). The controller may determine the number and frequency ofstops made (start-stop frequency) and the duration of each stop. In oneexample, the driver may be traveling via a busy street with frequenttraffic stops where the vehicle stops for multiple shorter durations. Inanother example, the driver may make fewer stops but of longerdurations. With an increase in the number of stops, the probability oftermination of a regeneration may increase. The driver's frequency, andforce of application of each of the accelerator pedal and the brakepedal may also be determined. In one example, a driver may be leadfooted and may accelerate and brake frequently. In another example, adriver may maintain a steady vehicle speed over a time period withouthard accelerations and decelerations. Also, the overall traffic velocity(average speed at which other on-road vehicles are travelling) and thedistance between two consecutive vehicles travelling in a lane may bedetermined. As such, during lower traffic velocities, if the distancebetween consecutive vehicles is smaller, there may be higher possibilitythat the driver needs to apply brakes more frequently. In contrast,during higher traffic velocities, and when the distance betweenconsecutive vehicles is higher, the vehicle may travel at a steady speedfor a longer duration.

At 322, the controller may determine possible transitions in driverstate of mind (e.g. from the initial state S_(D0) to a state S_(DK))based on the learned driver interactions with traffic and further basedon environmental factors and behavioral factors. In one example, thedriver state of mind may not change during the entire drive cycle whilein another example, the driver state of mind may frequently change fromone state to another. A non-homogeneous state transition model may beused to determine a probability of transition from one state of mind toanother. In one example, a simplified homogeneous transition matrix (T)may be used to predict possible transitions from one state of mind toanother.

An example of a homogeneous transition matrix (T) 700 is shown in FIG.7. In the homogeneous transition matrix (T), a finite probability isassigned to each transition from one state to another. The probabilitiesmay be based on driver history (as retrieved from the database) forstate of mind transitions while operating on the same route. The finiteprobabilities in the homogeneous transition matrix may not change basedon driver behavior (driver interactions with traffic) or conditions suchas weather in the current drive cycle.

As seen from the matrix 700, the first row 702 denotes the probabilitiesof transition from a current state of mind of S_(D0) to the statesS_(D1) and S_(D2). If the current state is assigned to be S_(D0), thechance that the driver will remain in the S_(D0) state during the entiredrive cycle is 70%, while the chance that the drive may transition tothe state S_(D1) at any point during the drive cycle is 20%, and thechance that the drive may transition to the state S_(D2) directly fromstate S_(D0), at any point during the drive cycle, is 10%. The secondrow 704 denotes the probabilities of transition from a current state ofmind of S_(D1) to the states S_(D0) and S_(D2). If the current state isassigned to be S_(D1), the chance that the driver will remain in theS_(D1) state during the entire drive cycle is 50%, while the chance thatthe drive may transition to the state S_(D0) at any point during thedrive cycle is 25%, and the chance that the drive may transition to thestate S_(D2) at any point during the drive cycle is 25%. The third row706 denotes the probabilities of transition from a current state of mindof S_(D2) to the states S_(D0) and S_(D1). If the current state isassigned to be S_(D2), the chance that the driver will remain in theS_(D2) state during the entire drive cycle is 50%, while the chance thatthe drive may directly transition to the state S_(D0) at any pointduring the drive cycle is 25%, and the chance that the drive maytransition to the state S_(D1) at any point during the drive cycle is25%.

The transition from one driver state of mind to another may be estimatedbased on a dynamic equation. For example, if the driver is in an initialmental state S_(D0), the transition to a state S_(Dk) may be estimatedusing equation 2.S _(Dk)(t+n)=S _(Dk) *T ^(n)  (2)where S_(Dk) is a state of mind of a driver, t is time, n is the numberof possible driver states of mind, and T is the homogenous transitionmatrix.

In another example, a non-homogeneous transition matrix (Tn) may be usedto predict possible transitions from one state of mind to another. Thenon-homogenous matrix may take into account real-time driverinteractions with traffic, and other evolutionary conditions such asweather while determining a transition in state from an initial driverstate to an updated driver state of mind. A non-homogeneous transitionmodel may be experimentally identified and validated via tools includingmachine learning and exploratory data analysis. Such tools may be usedto determine probabilities of transition from one state to another. Theprobabilities of transition for the non-homogeneous transition matrix(Tn) may not be definite numbers and may change in real-time. In oneexample, the probability of transition from an initial state (S_(Dj)) toan updated state (S_(Dk)), as shown in equation 3, may be a function ofthe driver interactions with traffic, weather conditions, driver tripconstraints, and time of day.P(S _(Dk) |S _(Dj))=f(driver interactions with traffic, weather, tripconstraints, time of day)   (3)

FIG. 6 shows a state machine diagram 600 for transition of a drivermental state from one state to another. In the diagram 600, threepossible driver states of mind, S_(D0), S_(D1), and S_(D2), are shown.In one example, if it is determined (based on driver history) that aninitial driver state of mind is the first state S_(D0), the driver maycontinue to operate in the first state of mind throughout the drivecycle. The first drive state of mind S_(D0) is optimal for attainingincreased PF regeneration efficiency. As such, in the first state, thedriver may maintain a steady vehicle speed over a time period withouthard accelerations and decelerations. The driver may continue to operatein the first state if there is no significant change in weatherconditions and/or traffic conditions that may impose constraints andincrease the duration of the trip. The probability that the driver willremain in the first state of mind is given by P(S_(D0)|S_(D0)).

The state of mind may transition from the first state S_(D0) to thesecond state S_(D1) in response to changes in driver interactions withtraffic and/or weather. The second state S_(D2) may be a sub-optimalstate of mind and may have an adverse effect on PF regenerationefficiency. In one example, due to a change in weather, such as if itstarts raining heavily during the drive, there may be changes in driverinteractions with traffic. As such, the driver may accelerate within ashort time and then frequently apply the brake to control the speed.Frequent application of brake may be detrimental to PF regenerationefficiency. In another example, an expected traffic congestion may causethe driver to stop for longer than anticipated, and after passingthrough the traffic congestion, the driver may drive aggressively toreach the destination without further delay. The probability oftransition from the first state to the second state is given byP(S_(D1)|S_(D0)). Once in the second state, the driver may continue tooperate in the second state for the remaining portion of the drivecycle. The probability that the driver will remain in the second stateof mind is given by P(S_(D1)|S_(D1)). After operating in the secondstate for some time, the driver may transition to the first state S_(D0)in response to changes in driver interactions with traffic and/orweather. In one example, due to changes in traffic conditions, such asfewer number of cars on the road, the driver may start driving lessaggressively and there may be a consequent change in state of mind.

The driver state of mind may also transition from the second stateS_(D2) to the third state S_(D2) in response to changes in driverinteractions with traffic and/or weather conditions. The third stateS_(D3) may be the least optimal state of mind and may adversely affectPF regeneration efficiency. In one example, due to a further changes inweather, such as if it starts snowing during the drive, the driver maybe making frequent stops without significantly accelerating. In anotherexample, triggers such as a phone call may cause the driver to startdriving aggressively with increased braking frequency. The probabilityof transition from the second state to the third state is given byP(S_(D2)|S_(D1)). Once in the third state, the driver may continue tooperate in the third state for the remaining portion of the drive cycle.The probability that the driver will remain in the third state of mindis given by P(S_(D3)|S_(D3)). After operating in the third state forsome time, the driver may transition to the second state S_(D1) inresponse to changes in driver interactions with traffic and/or weather.In one example, the weather conditions affecting the driver interactionswith traffic may change and the driver may make fewer stops in the drivecycle. The probability of transition from the third state to the secondstate is given by P(S_(D1)|S_(D2)). The state of mind may alsotransition from the third state directly to the first state facilitatingimproved PF regeneration. In one example, a road blockage may be liftedsuch that the driver is able to optimally accelerate without frequentbraking during the remaining portion of the drive cycle. The probabilityof transition from the third state to the first state is given byP(S_(D0)|S_(D2)) As such, unexpected changes in traffic situation, suchas a road closure, may lead to a transition from the first state to thethird state. Due to the unexpected changes in traffic, the driver maytake an alternate route and the driving aggressiveness may alsoincrease. A change of state from the first to the third state may resultin termination of an ongoing PF regeneration. The probability oftransition from the first state to the third state is given byP(S_(D2)|S_(D1)). In this way, during the drive cycle, driverinteractions with traffic, and environmental conditions may causechanges in the driver state of mind.

Based on the learned driver interactions with traffic, during the drivecycle, the controller may determine an updated driver state of mind. Thecontroller may use the non-homogeneous transition model to determine theupdated (current) driver state of mind. As such, there may be pluralityof changes in the driver state of mind (there may be additional sates ofmind, not depicted here) during the drive cycle, and the controller maycontinuously update the current driver state of mind as described abovebased on changes in operating conditions.

Returning to 324, the controller may determine an updated regenerationimpact factor corresponding to the updated driver state of mind. In oneexample, the controller may use a look-up table to determine the updatedregeneration impact factor corresponding to the updated driver state ofmind, with input being the updated driver state of mind and the outputbeing the updated regeneration impact factor. A second set of scalingfactors may be associated with the updated regeneration impact factor.The total cost function of each route ranked and displayed before thechange in the driver state of mind may be re-estimated based on changesin the weightages due to the scaling by the second set of scalingfactors. Also, in response to the updated driver state of mind, newroutes may be retrieved from the database and ranked along with thepreviously displayed routes. Alternatively, the previously displayedroutes may be re-ranked and displayed in a different order. Weightagesscaled by the second set of scaling factors corresponding to the updatedregeneration impact factor may be used to estimate a total cost functionassociated with each of the routes. Estimation of a total cost functionbased on individual cost functions associated with expected PFregeneration level of a route, probability of a PF regeneration processto be terminated during the drive cycle, cost to heat the PF to theregenerating temperature, fuel consumption, and duration of the trip maybe carried out using Equation 1. The details of total cost functionestimation is described in step 212 of FIG. 2.

Once the total cost functions are estimated for the plurality ofavailable routes between the origin and the destination, the routes maybe re-ranked based on the total cost function, the highest ranked routecorresponding to the lowest total cost function. In one example, theranking of the routes may change following the update in the driverstate of mind. In another example, the ranking of the routes may notchange following the update in the driver state of mind. At 325, theupdated ranking of the routes as estimated based on the updated totalcost functions may be displayed to the driver.

FIG. 5B shows a screenshot 550 of an example on-board navigation system,wherein updated route options are displayed after a change in the driverstate of mind. At the onset of the drive cycle, based on the initialdriver state of mind, the routes selected between the origin and thedestination were ranked and displayed as shown in FIG. 5A. Afterselecting the first route (from the displayed routes) corresponding tothe highest degree of attainable PF regeneration, the driver may proceedalong the selected route. However, after operating in the selected firstroute for a duration, there may be change in driver state of mind due tochanges in traffic and/or environmental situations and based on theupdated driver state of mind, the previously highest ranked route may nolonger remain the route corresponding to the highest degree ofattainable PF regeneration. In one example, the change in driver stateof mind may be associated with the driver starting to drive moreaggressively due to the presence of more traffic. Accordingly, theupdated routes may include alternate routes with fewer vehicles that ismore suited to steady state operation and achievement of the desiredlevel of PF regeneration.

As seen in screenshot 550, in each row, a first box 501 indicates thesuggested route between the origin and the destination, a second box 503shows a PF regeneration percentage achievable during the drive, and athird box 505 shows the time required to reach the destination from theorigin. The driver may select one of the four routes via a fourth boxincluded in each row.

As such, previously displayed routes may be displayed once again but theranking of the route may change. In one example, the route having thesecond rank in FIG. 5A may now be displayed as the highest ranked routein FIG. 5B while the route displayed as the highest ranked route in FIG.5A may be displayed as the second ranked route in FIG. 5B. The routedisplayed as the third ranked route in FIG. 5A may no longer bedisplayed as a route option. The route displayed as the fourth rankedroute in FIG. 5A may be displayed as the third ranked route in FIG. 5Bwhile a new route between the current geographical location and thedestination may be retrieved form the database and included as a new(fourth ranked) option. In this example, the driver may select the firstroute corresponding to the highest degree of attainable PF regeneration.Since the first route in the updated list is different from thepreviously selected route, updated navigation instructions may beprovided to divert the driver from the current route. The driver maycontinue to drive along the newly selected route to the destination. Inthis way, the ranking of the routes may be updated in real-time based ona current driver state of mind and the updated rankings may be displayedto the driver. Similar updates in the recommended list of routes mayoccur multiple times during the drive cycle as the driver state of mindchanges based on real-time conditions.

In this way, an updated driver state of mind and a correspondingregeneration factor may be selected from a database by applying anon-homogeneous transition model based on a probability of transitioningfrom a first driver state of mind (and corresponding first regenerationfactor) to an updated driver state of mind (and corresponding updated,second regeneration factor), the probability based on real-time driverinteractions with traffic.

Returning to FIG. 3, at 326, the routine includes determining if thereis a key-off event indicating that the vehicle operation has stopped. Ifa key-off event is not indicated, at 328, the controller may continue tolearn, in real-time, a current driver state of mind and based on theupdated driver state of mind, route recommendations may be updated inreal-time. Updating may include updating the ranking of previouslydisplayed routes or displaying new routes (retrieved from the database)to the driver in order to increase the attainable level of PFregeneration during the given drive cycle.

At 330, the database may be updated with information learnt during thecurrent drive cycle including the driver interactions with traffic,displayed routes, road segments traveled, different driver states ofmind, actual particulate filter regeneration attained, etc. Further, thecircumstances and probabilities of transition (from one state of mind toanother) leading to each change in the driver state of mind may beincluded in the update. The details of updating the database withinformation learned during the current drive cycle is discussed indetail at FIG. 4.

In this way, at an onset of a drive cycle, a first particulate filterregeneration factor may be selected based on a past driving history ofan operator, and one or more driving routes selected from a database maybe displayed to the operator, the one or more routes ranked based oneach of drive cycle origin and destination, respective regenerationcompletion efficiencies and the first regeneration factor. Navigationalinstructions for an operator selected route from the displayed one ormore driving routes may be displayed to the operator. During the drivecycle, a second particulate filter regeneration factor may be selectedbased on real-time driver interactions with traffic while travellingalong the operator selected route and accordingly route and navigationalinstructions may be updated.

FIG. 4 shows an example method 400 for updating the database offrequently travelled routes. At 402, the routine may include determiningif there is a vehicle key-on event. For example, it may be determined ifthe vehicle driver has expressed an intent to start vehicle operation.As such, by confirming a vehicle key-on event, an upcoming vehicle drivecycle is indicated. If a vehicle key-on event is not detected, andtherefore an upcoming vehicle drive cycle, is not confirmed, the methodmay end and the database may not be updated.

If the vehicle key-on event is confirmed, at 404, the controller maylearn origin characteristics including time and geographic location ofthe key-on event. For example, based on information from a vehiclenavigation system (e.g., GPS device), the controller may determine theorigin characteristics. In this way, the controller may determine anamount of time the vehicle was stopped at a location (e.g., the point oforigin) before beginning a trip. Also, a duration elapsed since theimmediately preceding key-off event may be determined. That is, astopped duration of the vehicle at the current location may beestimated.

At 406, the controller may learn details regarding a route of vehicletravel including road segments traveled. This may include topographicalinformation (such as road gradient, incline, terrain) of each of theroad segments in the actual route of travel. The details may be learnedbased on information from the vehicle navigation system and/or from anexternal server via wireless communication. At 408, the controller maylearn details about intermediate stops on the way to the destination.The stops may be due to traffic signals, traffic congestions, or thestops may be intentionally made by the driver. The geographical locationand also the duration of each of the stops may also be learned. At 410,traffic information for the route including the number of traffic stopsmay be learned via the navigation system. The controller may alsodetermine the speed limits of each road segment and the actual speed ofvehicle travel.

At 412, the controller may learn the time of travel including a time ofday when the vehicle is travelling, a date of travel, which day of theweek the vehicle is travelling, etc. At 414, the controller may learnengine operating conditions such as engine speed, engine load, enginetemperature, etc., from a plurality of engine sensors (such as sensor 16in FIG. 1) during each road segment travelled. Also, the controller maylearn the soot level accumulated on the PF during the vehicle travel.

At 416, the controller may learn the level of PF regeneration achievedduring the current travel. In one example, the level of PF regenerationmay be determined based on the change in

PF soot level (between soot level measured at the onset of the drive andat the end of each road segment) as estimated via an exhaust pressuresensor. In another example, the level of PF regeneration may bedetermined based on the duration of PF regeneration. Also, it may belearned if the PF regeneration process has been prematurely terminateddue to unfavorable operating conditions during the drive cycle. Thereasons for PF regeneration termination and the engine operatingconditions and the road conditions at which the PF regeneration eventwas terminated may also be recorded.

At 418, the controller may learn the driving characteristics of thedriver. These may include, for example, frequency of brake andaccelerator pedal application, frequency of brake and accelerator pedalrelease, transmission gear change frequency, duration of operation inelectric mode versus engine mode, etc. The controller may also learn thedifferent states of mind of the driver and the duration of each state ofmind, during the drive cycle based on the driving characteristics.Further, the conditions (traffic, environmental, behavioral, etc.)triggering the changes in driver state of mind may also be learned.Further, at each driver state of mind, the controller may learn theprobabilities of transition from the current state of mind to another asa function of the current state of mind and the conditions triggeringthe change in the driver state of mind.

At 420, the routine includes determining if there is a key-off eventindicating that the vehicle operation has stopped. If a key-off event isnot indicated, at 422, the controller may continue collecting dataregarding various aspects of vehicle operation during vehicle travel. Ifa vehicle stop is confirmed, at 424, the method includes learningdestination characteristics including geographical location of thedestination.

At 425, the controller may learn the fuel consumed during the trip fromthe origin to the destination. As an example, the amount of fuelconsumed may be estimated based on the initial and the final fuel levelin the fuel tank. In another example, fuel consumption may be estimatedbased on engine operating conditions. Also, the duration of travel, andthe time taken to reach the destination from the origin may be learned.

At 426, the database may be updated with all the aforementioned data (ascollected in steps 404 to 424) including information regarding currentroute of travel, engine operating conditions, PF regenerationinformation, and driver driving characteristics. At 428, the currentroute for travelling between the origin and the destination may becompared to one or more routes (between the origin and the destination)previously stored in the database. Cost function as estimated byEquation 1 may be estimated for the current route and compared to thecost function of each of the routes previously saved in the database. At430, based on the cost function comparison, the different routes may beranked in terms of highest fuel efficiency, shortest duration of travel,highest level of PF regeneration level achieved and any other operatorselected cost function. Also, a Markov chain based route rankingalgorithm may be used. As an example, a route in which the target levelof PF regeneration may be possible may not be the most fuel efficientroute.

In this way, at vehicle key-off, the database may be updated with routeinformation, origin characteristics, destination characteristics, driverbehavior, level of PF regeneration achieved, engine operatingconditions, date and time information, and traffic information, etc.

FIG. 10 shows a prophetic example of prediction and dynamic selection ofa proposed route suitable for optimal particulate filter regeneration. Aroute may be selected from an existing route database based on PFregeneration requirements of the engine, and a current driver state ofmind and the selected route may be displayed to the driver. Thehorizontal (x-axis) denotes time and the vertical markers t1-t6 identifysignificant times in the operation of the vehicle system.

At time t1, the driver starts the vehicle (such as a key-on event) andthe current geographical location of the vehicle, as determined via anon-board navigation system, is denoted by A. The driver initiallyindicates, via an input to the navigation system, the geographicallocation of the destination as denoted by B. The controller retrievesdrive history (for the driver) including characteristics and preferencesof the driver (such as frequency of brake usage, average accelerationforce used, average lane change frequency, etc.) from the database. Thecontroller may determine the current day and time of travel, origincharacteristics (weather conditions and traffic conditions), and basedon the retrieved data (above mentioned information), the controllerassigns an initial state of mind to the driver. The controller thenselects one or more routes from the database using dynamic programmingbased on PF soot level, fuel consumption, time of travel, and trafficconditions. Line 1020 shows change in soot level deposited on the PF.Dotted line 1021 denotes a threshold above which PF regeneration isdesired. Based on the current PF soot level (at time t1), the controllerinfers that PF soot level may increase to the threshold soot levelduring the trip from point A to point B and PF regeneration needs to becarried out. The controller then ranks the one or more selected routesas a weighted function of each of a particulate filter regenerationefficiency, a probability of completion of a PF regeneration event, afuel efficiency, and a travel time. While ranking the one or moreselected routes, the weighted function is adjusted based on aregeneration efficiency factor corresponding to the initial state ofmind of the driver. In this example, the initial driver state of mind isan optimal state of mind (S_(D0)) wherein the particulate filterregeneration efficiency is highest and the probability of completion ofa PF regeneration event is also elevated. The particulate filterregeneration efficiency is an amount of particulate filter regenerationachieved in the drive cycle via a combination of passive and activeregeneration. In passive regeneration, the soot is burnt due to higherexhaust temperature during higher load conditions and during activeregeneration, temperature of the PF may be increased by flowingelectricity through it. In response to the upcoming requirement of PFregeneration, during ranking of the one or more routes, a higherweightage is assigned to each of the particulate filter regenerationefficiency and the probability of completion of a PF regeneration eventand a lower weightage is assigned to fuel efficiency and travel time. Inthis way, the controller displays the route with the highest efficiencyof particulate filter regeneration and probability of completion of a PFregeneration event at the highest ranked and the route with the lowestefficiency of particulate filter regeneration and probability ofcompletion of a PF regeneration event at the lowest rank. The list isthen displayed to the driver so that they can select a route based onthe ranking.

At time t1, the driver selects route 402 which is the highest rankedroute in the list of suggested routes between the origin A anddestination B as provided to the driver. However, at time t2, it isobserved that the driver deviates from the originally selected route andtakes a new route. In response to the change in route, the controllerpredicts the upcoming route segments between the current location to thedestination B based on the driver's driving history retrieved from thedatabase and current traffic conditions. As such, the frequentlytravelled routes by the driver during the time and/or day of the week istaken into consideration while predicting the upcoming route segmentsusing stochastic dynamic programming. Based on driver history andinitial state of mind, the controller predicts that the driver may takeroute 404 to a first intermediate stop C and from there take route 406to the destination B. The controller then schedules PF regenerationbased on the predicted route.

The driver follows the predicted route and continues to drive via route404, however it is observed that after the first intermediate stop C, attime t3, the driver deviates from route 406. Also, at time t3, based ondriver interactions with traffic between time t1 and t3, the controllerupdates the state of mind of the driver from the optimal state of mindto a second, sub-optimal state of mind (S_(D1)). Due to the transitionto the second, sub-optimal state of mind, it may be learnt that thedriver may be operating the vehicle more aggressively and the drivingcharacteristics, such as an increased frequency of brake application,may reduce the particulate filter regeneration efficiency and maydecrease the probability of completion of a PF regeneration on the givendrive cycle. In response to the change in route, based on driver historyand the updated state of mind, once again the controller predicts thatthe driver may take route 408 to a second intermediate stop D and fromthere take route 410 to a third intermediate stop E and then take route412 to the destination B. However, it is observed that the driver doesnot take the predicted route segments 408, 410, and 412 but continues tothe destination via a new route 414 and 416. The driver makes a stop atan intermediate point F on the way to the destination B and finallyreaches the destination at time t4.

It is observed that the soot level in the PF reaches the threshold level421 at time t3, and passive regeneration of the soot is carried out.However, since the driver does not follow a suggested route optimal forPF regeneration and due to the sub-optimal state of mind of the driver,the level of PF regeneration achieved upon reaching the destination B attime t4 is lower than the level of PF regeneration that could have beenachieved if the driver would have taken route 1002 from the origin A tothe destination B. Also, due to the driver deviating from the suggestedand the predicted routes, scheduling of passive regeneration of the PF(between time t3 and t4) may not have been possible. Dotted line 1022shows a possible change in soot level in the PF that would have occurredif route 1002 was taken and assuming that the driver state of mind hadnot changed from the first to the second. As observed from the plots1020 and 1022, the amount of soot burnt between time t3 and t4 wouldhave been higher if suggested route 1002 was taken relative to theamount of soot burnt during travel via routes 1014 and 1016.

At time t4, upon reaching the destination B (at vehicle key-off) thedatabase may be updated with route information, origin characteristics(such as geographic location), destination characteristics, location ofeach stop taken, driver interactions with traffic (such as frequency ofgear change, pedal application and release frequency, brake applicationfrequency, etc.), level of PF regeneration achieved, engine operatingconditions (such as engine speed, engine load, engine temperature,etc.), date and time information, driver states of mind, and trafficinformation. Also, road gradient, terrain, incline information for eachsegment of the route may be included in the database. The data saved inthe database may be utilized for future route selection and/orprediction.

In this way, by dynamically selecting a travel route, based onparticulate filter regeneration requirement during a drive cycle, from aplurality of routes available in a database, PF regeneration may beeffectively scheduled and carried out. By accounting for a currentdriver state of mind representative of real-time driver drivingbehavior, the likelihood that a driver will select a recommended routewhere PF regeneration efficiency is higher, is increased. By estimatingthe driver state of mind in real-time based on driver interactions withtraffic, and environmental conditions, ranking of the navigationalroutes corresponding to the probability of attainment of a desired levelof PF regeneration may be updated as driver behavior changes. Bypredicting a destination or segments of an upcoming route based on drivehistory and drive statistics stored in the route database, it may bepossible to schedule PF regenerations even during trips where a finaldestination has not been specified by the driver or when the driverdeviates from a selected route. The technical effect of maintaining adatabase of driver mental states and frequently traveled routes withinformation including the possible degree of PF regeneration attainableon each route is that at vehicle key-on, an initial state of mind may beselected from the database based on driver history and a route may beselected from the database based on the PF soot level and driverselected cost function including highest fuel efficiency and lowesttravel time for the drive cycle. In this way, during higher thanthreshold PF soot load, by selecting a favorable route foropportunistically regenerating the PF, over-loading of soot in the PFmay be reduced thereby improving engine performance. By allowing for aPF to be regenerated opportunistically, using passive regeneration, theneed for active regeneration is reduced, providing additional fuelefficiency benefits.

An example engine method comprises: learning, after each drive cycle, aparticulate filter regeneration efficiency as a function of one or morecharacteristics of a travelled route and operator behavior over thetravelled route, updating a database based on the learning, and at onsetof a drive cycle, displaying to an operator one or more routes selectedfrom the database, the selection based on a particulate filter soot loadat the onset of the drive cycle. In any preceding example, additionallyor optionally, the selection is further based on an operator indicateddestination for the drive cycle relative to an origin of the drivecycle. In any or all of the preceding examples, additionally oroptionally, the selection is further based on an operator selected costfunction including one or more of a highest fuel efficiency and a lowesttravel time for the drive cycle. Any or all of the preceding examplesfurther comprises, additionally or optionally, ranking the one or moreroutes as a weighted function of each of a particulate filterregeneration efficiency, a probability of completion of a particulatefilter regeneration event, a fuel efficiency, and a travel time of eachof the one or more routes. In any or all of the preceding examples,additionally or optionally, the particulate filter regenerationefficiency includes a degree of particulate filter regenerationpredicted for the drive cycle, and wherein the one or morecharacteristics of the traveled route include a number of stops, a roadgradient, and traffic conditions. In any or all of the precedingexamples, additionally or optionally, each of the particulate filterregeneration efficiency and the probability of completion of aparticulate filter regeneration event are assigned higher weightageswhen the particulate filter soot load is higher than a threshold. In anyor all of the preceding examples, additionally or optionally, each ofthe fuel efficiency and the time of travel are assigned higherweightages when the particulate filter soot load is lower than thethreshold. Any or all of the preceding examples further comprises,additionally or optionally, in response to the operator not selecting aroute from the one or more routes displayed to the operator, dynamicallypredicting an upcoming route segment based on an operator drivinghistory retrieved from the database. In any or all of the precedingexamples, additionally or optionally, the operator driving historyincludes routes previously traveled as a function of one or more of timeof day, day of a week, and traffic conditions. Any or all of thepreceding examples further comprising, additionally or optionally, inresponse to the operator selecting a route from the one or more routesdisplayed to the operator, initiating travel along the route, and thendeviating from the selected route, dynamically predicting an upcomingroute segment based on an operator driving history retrieved from thedatabase. Any or all of the preceding examples further comprising,additionally or optionally, based on the route selected by the operatorfrom the one or more displayed routes, scheduling an active particulatefilter regeneration event during the drive cycle, wherein during theactive particulate filter regeneration event, a temperature of theparticulate filter is increased by flowing electric current through theparticulate filter. Any or all of the preceding examples furthercomprising, additionally or optionally, when a destination for the drivecycle is not specified by the operator, predicting the destination basedon an operator driving history as retrieved from the database, anddisplaying to the operator, one or more routes selected from thedatabase based on the predicted destination. In any or all of thepreceding examples, additionally or optionally, the onset of the drivecycle includes a vehicle key-on event, and wherein updating the databasebased on the learning includes, at a vehicle key-off event, updating thedatabase with route information, origin characteristics, destinationcharacteristics, operator behavior, level of particulate filterregeneration achieved, engine operating conditions, date and timeinformation, and traffic information.

Another example engine method comprises: in response to an operatordestination selection indicated via a display of a vehicle, estimatingan exhaust particulate filter soot load, determining a current locationof the vehicle, retrieving one or more routes from the current locationto the destination from a database, ranking the one or more routes basedon each of a particulate filter regeneration efficiency, fuelefficiency, and time of travel of each route, and displaying to theoperator the one or more routes to the selected destination in order oftheir rank. Any of the preceding examples further comprising,additionally or optionally, responsive to a higher than thresholdparticulate filter soot load, ranking the one or more routes byassigning a higher weightage to particulate filter regenerationefficiency and a lower weightage to each of the fuel efficiency and thetime of travel. Any or all of the preceding examples further comprises,additionally or optionally, responsive to a lower than thresholdparticulate filter soot load, ranking the one or more routes byassigning the higher weightage to each of the fuel efficiency and thetime of travel and assigning the lower weightage to the particulatefilter regeneration efficiency. Any or all of the preceding examplesfurther comprises, additionally or optionally, responsive to theoperator not selecting a route from the one or more displayed routes orthe operator deviating from a selected route, predicting one or moreroute segments from the current location to the destination based onoperator drive history, retrieved from the database, and schedulingpassive regeneration of the particulate filter based on the one or morepredicted route segments.

In yet another example, a vehicle method comprises: in response to adestination for a drive cycle not being indicated by an operator,determining a current location of the vehicle, retrieving a drivinghistory of the operator from a database, predicting a destination basedon the driving history, dynamically updating selection of one or moreupcoming route segments based on the current location of the vehiclerelative to the predicted destination, ranking the one or more upcomingroute segments based on each of a corresponding particulate filterregeneration efficiency, fuel efficiency, and time of travel, anddisplaying to the operator, the one or more upcoming route segments tothe predicted destination, hierarchically in order of their rank. Anypreceding example further comprising, additionally or optionally, inresponse to the operator not selecting a route from the one or moredisplayed upcoming route segments, dynamically updating the one or moreupcoming route segments based on the driving history, and schedulingactive regeneration of the particulate filter based on the updated oneor more route segments. Any or all of the preceding examples furthercomprises, additionally or optionally, at completion of a drive cycle,updating the database with route segment information including operatorbehavior, level of particulate filter regeneration achieved, engineoperating conditions over the completed drive cycle, date and time oftravel information, and traffic information over the drive cycle.

In a further example, an engine method comprises: at an onset of a drivecycle, displaying a first driving route responsive to each of aparticulate filter (PF) loading and past driving history; and duringtravel along the first driving route, displaying an updated routeresponsive to each of traffic conditions and a comparison of a real-timedriving history along the first route on the drive cycle relative to thepast driving history. In any preceding example, additionally oroptionally, displaying the first driving route includes selecting thefirst driving route from a database, including a plurality of drivingroutes, based on a first inferred driver state of mind, the firstinferred driver state of mind based on the past driving history, andwherein displaying the updated route includes selecting the updatedroute from the database based on an updated driver state of mind. In anyor all of the preceding examples, additionally or optionally, theupdated driver state of mind is selected from a plurality of inferreddriver states of mind stored in the database, the updated driver stateof mind selected based on a comparison of the real-time driving historyalong the first route on the drive cycle relative to the past drivinghistory, wherein each of the plurality of inferred driver states of mindhas an associated PF regeneration factor. In any or all of the precedingexamples, additionally or optionally, displaying the first route furtherincludes, responsive to an operator indicated destination for the drivecycle, displaying one or more routes retrieved from the plurality ofdriving routes included in the database, the one or more routes rankedas a first function of corresponding PF regeneration efficiency, aprobability of completion of a PF regeneration event during the drivecycle, and a first PF regeneration factor associated with the firstdriver state of mind, wherein the corresponding PF regenerationefficiency for each of the one or more routes is determined as afunction of the corresponding degree of PF regeneration predicted forthe drive cycle and the first PF regeneration factor. In any or all ofthe preceding examples, additionally or optionally, displaying theupdated route includes, responsive to the comparison, displaying the oneor more routes, the one or more routes ranked as a second function ofupdated PF regeneration efficiency, the probability of completion of thePF regeneration event during the drive cycle, and a second PFregeneration factor associated with the updated driver state of mind,wherein the corresponding PF regeneration efficiency for each of the oneor more routes is determined as a function of the corresponding degreeof PF regeneration predicted for the drive cycle and the second PFregeneration factor. In any or all of the preceding examples,additionally or optionally, ranking the one or more routes as thefunction of the first PF regeneration factor includes ranking the one ormore routes based on assigned weightages to each of the PF regenerationefficiency and the probability of completion of the PF regenerationevent during the drive cycle, the assigned weightages scaled by a firstfunction based on the first PF regeneration factor, and wherein rankingthe one or more routes as the function of the updated PF regenerationfactor includes ranking based on assigned weightages to each of the PFregeneration efficiency and the probability of completion of the PFregeneration event during the drive cycle, the assigned weightagesscaled by a second set of factors corresponding to the second PFregeneration factor, the second set different from the first set. In anyor all of the preceding examples, additionally or optionally, thereal-time driving history includes real-time driver interactions withtraffic including real-time accelerator pedal usage and real-time brakeusage during the drive cycle while driving along the first drivingroute, and wherein the past driving history includes frequency of brakeusage, average acceleration force used, and average lane changefrequency while driving along the first route in one or more drivecycles prior to the drive cycle. Any or all of the preceding examplesfurther comprises, additionally or optionally, learning, at completionof the drive cycle, a degree of PF regeneration attained during thedrive cycle and then updating the database with the learned degree of PFregeneration attained for the drive cycle, the first inferred driverstate of mind, and the updated driver state of mind.

In a yet further example, a method comprises: at an onset of a drivecycle, selecting a first particulate filter regeneration factor based ona past driving history of an operator; displaying, to the operator, oneor more driving routes selected from a database, the one or more routesranked based on each of drive cycle origin and destination, respectiveregeneration completion efficiencies and the first regeneration factor;displaying, to the operator, navigational instructions for an operatorselected route from the displayed one or more driving routes; and duringthe drive cycle, selecting a second particulate filter regenerationfactor based on real-time driver interactions with traffic whiletravelling along the operator selected route. In any preceding example,additionally or optionally, each of the first regeneration factor andthe second regeneration factor are selected from a plurality ofregeneration factors stored in the database, each of the plurality ofregeneration factors corresponding to a distinct driver state of mind.In any or all of the preceding examples, additionally or optionally,selecting the second regeneration factor includes applying anon-homogeneous transition model to select the second regenerationfactor from the plurality of regeneration factors based on a probabilityof transitioning from the first regeneration factor to the secondregeneration factor, the probability based on the real-time driverinteractions with traffic. In any or all of the preceding examples,additionally or optionally, the past driving history of the driverincludes routes traveled by the operator as a function of one or more ofa time of a day, the day of a week, the drive cycle origin anddestination, and drive characteristics including frequency of brakeusage, average acceleration force used, and average lane changefrequency. In any or all of the preceding examples, additionally oroptionally, the real-time driver interactions with traffic include oneor more of frequency of stops, frequency of lane changes, acceleratorpedal input, and brake input during the drive cycle. In any or all ofthe preceding examples, additionally or optionally, selecting the firstregeneration factor is further based on traffic conditions at the drivecycle origin, and environmental conditions at the drive cycle originincluding ambient temperature, ambient humidity, and precipitation. Inany or all of the preceding examples, additionally or optionally, theone or more routes being ranked further includes ranking each of the oneor more routes based on a weighted function of each of the respectiveregeneration completion efficiencies, a probability of completion of aparticulate filter regeneration event, fuel efficiency, and a time todestination of each of the one or more routes, the weighted functionscaled based on the first regeneration factor. Any or all of thepreceding examples further comprises, additionally or optionally, inresponse to selection of the second particulate filter regenerationfactor, updating the weighted function scaled based on the secondregeneration factor, and then updating the ranking of the one or moreroutes.

In another further example, a vehicle system comprises: a vehicle, anavigation system wirelessly connected to an external network, adisplay, an engine including an intake system and an exhaust system, theexhaust system including a particulate filter (PF) coupled to an exhaustpassage and a pressure sensor coupled to the exhaust passage upstream ofthe particulate filter, and a controller with computer readableinstructions stored on non-transitory memory for: at an onset of a drivecycle, displaying a first route based on PF load and a first driverstate of mind, and responsive to driver interactions with traffic whiletravelling on the first route, displaying a plurality of updated routesbased on a second driver state of mind, wherein the first driver stateof mind is selected from a database based on each of the PF load and adriver history and a change from the first driver state of mind to thesecond driver state of mind is based on the driver interactions withtraffic while traveling on the first route. In any preceding example,additionally or optionally, the first route is selected based on a firstweighted PF regeneration efficiency, the first weighted PF regenerationefficiency based on a first PF regeneration factor corresponding to thefirst driver state of mind. In any or all of the preceding examples,additionally or optionally, the plurality updated routes are selectedbased on a second weighted PF regeneration efficiency, the secondweighted PF regeneration efficiency based on a second PF regenerationfactor corresponding to the second driver state of mind, and wherein thedisplaying of plurality of the updated routes include ranking each routeof plurality of the updated routes based on the second weighted PFregeneration efficiency. In any or all of the preceding examples,additionally or optionally, the controller contains further instructionsfor: learning, during the drive cycle, the driver interactions withtraffic, displayed routes, road segments traveled, driver state of mind,particulate filter regeneration attained, and after completion of thedrive cycle, updating the database based on the learning.

In a further representation, the vehicle is a hybrid vehicle system. Inany preceding example, additionally or optionally, an example method fora hybrid vehicle comprises: at an onset of a drive cycle, selecting afirst value indicative of a driver state of mind based on a drivehistory, displaying, to the driver, one or more routes selected from adatabase, a regeneration factor of each of the one or more routes basedon the first value, and during the drive cycle, selecting, in real-time,a second value indicative of an updated driver state of mind based onreal-time driver interactions with traffic.

Note that the example control and estimation routines included hereincan be used with various engine and/or vehicle system configurations.The control methods and routines disclosed herein may be stored asexecutable instructions in non-transitory memory and may be carried outby the control system including the controller in combination with thevarious sensors, actuators, and other engine hardware. The specificroutines described herein may represent one or more of any number ofprocessing strategies such as event-driven, interrupt-driven,multi-tasking, multi-threading, and the like. As such, various actions,operations, and/or functions illustrated may be performed in thesequence illustrated, in parallel, or in some cases omitted. Likewise,the order of processing is not necessarily required to achieve thefeatures and advantages of the example embodiments described herein, butis provided for ease of illustration and description. One or more of theillustrated actions, operations, and/or functions may be repeatedlyperformed depending on the particular strategy being used. Further, thedescribed actions, operations, and/or functions may graphicallyrepresent code to be programmed into non-transitory memory of thecomputer readable storage medium in the engine control system, where thedescribed actions are carried out by executing the instructions in asystem including the various engine hardware components in combinationwith the electronic controller.

It will be appreciated that the configurations and routines disclosedherein are exemplary in nature, and that these specific embodiments arenot to be considered in a limiting sense, because numerous variationsare possible. For example, the above technology can be applied to V-6,I-4, I-6, V-12, opposed 4, and other engine types. The subject matter ofthe present disclosure includes all novel and non-obvious combinationsand sub-combinations of the various systems and configurations, andother features, functions, and/or properties disclosed herein.

The following claims particularly point out certain combinations andsub-combinations regarded as novel and non-obvious. These claims mayrefer to “an” element or “a first” element or the equivalent thereof.Such claims should be understood to include incorporation of one or moresuch elements, neither requiring nor excluding two or more suchelements. Other combinations and sub-combinations of the disclosedfeatures, functions, elements, and/or properties may be claimed throughamendment of the present claims or through presentation of new claims inthis or a related application. Such claims, whether broader, narrower,equal, or different in scope to the original claims, also are regardedas included within the subject matter of the present disclosure.

The invention claimed is:
 1. A method, comprising: at an onset of adrive cycle, an electronic controller displaying a first driving routeresponsive to each of a particulate filter (PF) loading and past drivinghistory; and during travel along the first driving route, the electroniccontroller displaying an updated route responsive to each of trafficconditions and a comparison of a real-time driving history along thefirst driving route on the drive cycle relative to the past drivinghistory.
 2. The method of claim 1, wherein displaying the first drivingroute includes selecting the first driving route from a database,including a plurality of driving routes, based on a first inferreddriver state of mind, the first inferred driver state of mind based onthe past driving history, and wherein displaying the updated routeincludes selecting the updated route from the database based on anupdated driver state of mind.
 3. The method of claim 2, wherein theupdated driver state of mind is selected from a plurality of inferreddriver states of mind stored in the database, the updated driver stateof mind selected based on the comparison of the real-time drivinghistory along the first driving route on the drive cycle relative to thepast driving history, wherein each of the plurality of inferred driverstates of mind has an associated PF regeneration factor.
 4. The methodof claim 3, wherein displaying the first driving route further includes,responsive to an operator indicated destination for the drive cycle,displaying one or more routes retrieved from the plurality of drivingroutes included in the database, the one or more routes ranked as afirst function of corresponding PF regeneration efficiency, aprobability of completion of a PF regeneration event during the drivecycle, and a first PF regeneration factor associated with the firstinferred driver state of mind, wherein the corresponding PF regenerationefficiency for each of the one or more routes is determined as afunction of a corresponding degree of PF regeneration predicted for thedrive cycle and the first PF regeneration factor.
 5. The method of claim4, wherein displaying the updated route includes, responsive to thecomparison, displaying the one or more routes, the one or more routesranked as a second function of updated PF regeneration efficiency, theprobability of completion of the PF regeneration event during the drivecycle, and a second PF regeneration factor associated with the updateddriver state of mind, wherein the corresponding PF regenerationefficiency for each of the one or more routes is determined as afunction of the corresponding degree of PF regeneration predicted forthe drive cycle and the second PF regeneration factor.
 6. The method ofclaim 5, wherein ranking the one or more routes as the function of thefirst PF regeneration factor includes ranking the one or more routesbased on assigned weightages to each of the PF regeneration efficiencyand the probability of completion of the PF regeneration event duringthe drive cycle, the assigned weightages scaled by a first functionbased on the first PF regeneration factor, and wherein ranking the oneor more routes as the function of the updated PF regeneration factorincludes ranking based on assigned weightages to each of the PFregeneration efficiency and the probability of completion of the PFregeneration event during the drive cycle, the assigned weightagesscaled by a second set of factors corresponding to the second PFregeneration factor, the second set different from a first set.
 7. Themethod of claim 2, further comprising, learning, at completion of thedrive cycle, a degree of PF regeneration attained during the drive cycleand then updating the database with the learned degree of PFregeneration attained for the drive cycle, the first inferred driverstate of mind, and the updated driver state of mind.
 8. The method ofclaim 1, wherein the real-time driving history includes real-time driverinteractions with traffic including real-time accelerator pedal usageand real-time brake usage during the drive cycle while driving along thefirst driving route, and wherein the past driving history includesfrequency of brake usage, average acceleration force used, and averagelane change frequency while driving along the first driving route in oneor more drive cycles prior to the drive cycle.
 9. A vehicle system,comprising: a vehicle; a navigation system wirelessly connected to anexternal network; a display; an engine including an intake system and anexhaust system, the exhaust system including a particulate filter (PF)coupled to an exhaust passage and a pressure sensor coupled to theexhaust passage upstream of the particulate filter; and a controllerwith computer readable instructions stored on non-transitory memory for:at an onset of a drive cycle, displaying a first route based on PF loadand a first driver state of mind; and responsive to driver interactionswith traffic while travelling on the first route, displaying a pluralityof updated routes based on a second driver state of mind, wherein thefirst driver state of mind is selected from a database based on each ofthe PF load and a driver history, and a change from the first driverstate of mind to the second driver state of mind is based on the driverinteractions with traffic while traveling on the first route.
 10. Thesystem of claim 9, wherein the first route is selected based on a firstweighted PF regeneration efficiency, the first weighted PF regenerationefficiency based on a first PF regeneration factor corresponding to thefirst driver state of mind.
 11. The method of claim 9, wherein theplurality of updated routes is selected based on a second weighted PFregeneration efficiency, the second weighted PF regeneration efficiencybased on a second PF regeneration factor corresponding to the seconddriver state of mind, and wherein the displaying of the plurality ofupdated routes includes ranking each route of the plurality of updatedroutes based on the second weighted PF regeneration efficiency.
 12. Thesystem of claim 9, wherein the controller contains further instructionsfor: learning, during the drive cycle, the driver interactions withtraffic, displayed routes, road segments traveled, driver state of mind,particulate filter regeneration attained; and after completion of thedrive cycle, updating the database based on the learning.